The 1970s were a period of major upheaval in the United States. Prices were going up, output was going down, and faith in the U.S. political system was going out the window. In the midst of the political upheaval, and with a loss of faith in Keynesian prescriptions for economic stabilization, a heretofore-obscure Wall Street Journal editor mapped an economic strategy for Republican resurgence and the Reagan revolution—famously depicted in this clip from Ferris Bueller's Day Off:

I was recently recommended this roadmap, the ostentatiously named How the World Works by Jude Wanniski. Regardless of one’s political persuasions, the book is worth reading if for no other reason than its marked impact on economic policy in the United States and around the world. A justifiably controversial figure—he found common ground in economics with Louis Farrakhan and endorsed John Kerry for President after calling President Bush an imperialist—Wanniski was foremost a conservative intellectual who disseminated his ideas advising the presidential campaigns of Ronald Reagan, Jack Kemp, Steve Forbes, and Bob Dole. In the case of President Reagan, Wanniski fell out of favor because he famously lacked modesty; his ideas had no such ill fortune. So what were these ideas?

Supply-Side Economics

John Maynard Keynes, arguably the most influential economist of the 20th century, posited during the 1930s that the great depression was the result of suppressed aggregate demand and inadequate government intervention. As a solution, he suggested that government deficit spending could compensate for diminished consumer spending to boost growth. While this is a very simple accounting of the Keynesian model, it is the basic premise. Wanniski viewed the cause of the great depression—and many other historic upheavals from ancient Rome to the stagflation of the 1970s— differently. Wanniski believed tax policy, and more specifically tax hikes—whether through progressivity, government growth presaging future tax hikes, or inflation pushing incomes into higher real rates— have invited all major economic collapses and faltering empires. Consequently, the best way to encourage growth is to concentrate policy toward creating the right incentives for exchange in the marketplace through low taxation, free trade, and a gold-backed currency to maintain faith in exchange and the debtor/creditor balance. Focus would be shifted to producers (the supply-side) and everyone would benefit as growth increased government revenue, in spite of rate reductions.

The Laffer Curve

Arguing against a proposed tax increase by then President Ford, economist Arthur Laffer sketched the following curve onto a napkin at a dinner table with Wanniski and Ford aides Dick Cheney and Donald Rumsfeld:

The Laffer Curve--as Wanniski coined it— is on the surface uncontroversial, while in its application it can be extraordinarily so. The curve shows that, for any given point in time, there is one specific tax rate that will maximize government revenues. At points on the curve to the left of the maximizing rate, if the already low tax rate is increased, revenues will increase. If, however, the tax rate is to the right of the maximizing point, a further increase in the tax rate will actually reduce government revenues—producers ‘on the margin’ will no longer be incentivized to grow or start their enterprise and will, according to the model, produce less or evade taxation. In contrast, if the tax rate is at a point to the right of the maximizing point and the tax rate is cut, the ensuing economic growth will outweigh the rate reduction and revenue will increase. It is on this last point that the curve engenders controversy. George H.W. Bush, running for President in 1980, termed it “voodoo economics” and the political left has long derided it as “trickle-down economics”. Either way, the Laffer Curve was fully embraced by President Reagan and has made tax-cutting a mantra of nearly every Republican, and some Democrats, ever since.

What does the economic evidence say?

There is little dispute that there is a revenue-maximizing rate of taxation. At 0% no revenue is collected for obvious reasons and at 100% people are either producing for the glory of their despot or doing nothing. Somewhere in between there is an optimal rate that accounts for the elasticity of response (i.e. an X% tax change leads to an X% change in compliance/production), but the political left and right are at loggerheads over what that rate is. Dylan Matthews of the Washington Post collected an array of purported expert opinions of what this rate is (for various taxes, but here I will focus on the top federal personal income tax bracket) with the responses ranging from 15% to 70%. Econometrically arriving at a specific rate is extraordinarily difficult because production and (tax) evasion are influenced by virtually every policy. Controlling for this vast array of relevant variables to identify the causal impact of a tax change on revenue is thus nearly impossible—and never without controversy.

Read my lips: no new taxes!

Wanniski, who was not a trained economist and never claimed to be, provides a vast array of anecdotal evidence to support his theory—including the tracking of market fluctuations during congressional debate over the Smoot-Hawley Tariff Act in 1929 and the debasement of Roman currency post-Aurelius in 180 A.D. Regardless of what you think of his arguments and their subsequent policy consequences, the book is worth a read. As the title implies, Wanniski takes a wide and long view of the world and provides plenty of uncontroversial and interesting analysis of past political events and economic forays.

Of the actors nominated for ‘Best Actor-leading role’ at the Oscars— the premier international awards for film— I was curious to learn more about their measurable attributes (age, national origin, film credits), the commercial success of the film they starred in, and how these variables might or might not be correlated with ultimately winning the award. The Oscars are nothing without the self-reverence and controversy they inspire. For example, my favorite performance from 2014— Jake Gyllenhaal in Nightcrawler— was likely snubbed, not as a result of his exemplary psychopathic depiction, but as a function of the odd voting regime and the film’s overall ‘momentum’. Even after receiving a nomination, there is substantial cynicism surrounding the underlying biases that seem to dictate winners. For example, I assumed that actors from commercially successful films were disadvantaged— the academy (the Oscars’ voting body) deeming popularity the equivalent of lowbrow and thus unworthy of acting excellence.

Who gets nominated?

I created a dataset of the Best Actor nominees from 2000-2014, using IMDB, that included the following variables: (1) the actor’s age at the time of the film, (2) the actor's number of previous feature film acting credits, (3) the most up-to-date gross box office revenue in the United States- adjusted for inflation to 2000 US dollars (4) whether they won the oscar, (5) whether they were American, and (6) whether they had previously won for best actor (supporting or lead). This added up to 75 observational units and 450 data points.

The descriptive statistics were as follows:

The winners are shockingly representative of the overall pool of nominees. Immediately, it seemed clear that it would be incredibly unlikely for any of these variables to be a statistically valid indicator of the likelihood of winning the award. While the two groupings had very similar averages, the standard deviations—a measurement of the clustering of observations around the mean— for all nominees were larger than for the winners. For example, the standard deviation for the winner’s age was 7.6 years versus 12.2 for all nominees and the standard deviation for box office revenue was $50.5 million for the winners versus $58.8 million for all nominees.

Why does this matter? It might imply that while the academy is open to nominating a wide range of actors representing a variety of films (a large standard deviation), the winners are more likely to fit an archetype (a small standard deviation). Here is a look at a chart of the inflation adjusted box office revenue of the winners and those nominated:

The three largest revenue films were, in order of highest revenue: Pirates of the Caribbean (Depp, 2003), American Sniper (Cooper, 2014), and Cast Away (Hanks, 2000). The actor from the highest revenue film to win was Russell Crowe for Gladiator (2000), which earned $187.7 million in domestic box office. The lowest grossing film to earn a nomination for best actor was A Better Life (Bichir, 2011), which earned a paltry $1.3 million in the U.S. The trend line—the black dash cutting through the chart— shows that while there is variation from year-to-year, the revenues have remained fairly constant in real terms. And of course, as I mentioned before, the winners are more closely clustered around the trend line. Now for a look at the actor’s ages:

Here, the trend line indicates that the nominees have gotten slightly older over time, despite Eddie Redmayne winning for the Theory of Everything in 2014 at age 32. The oldest winner was Jeff Bridges (Crazy Heart, 2009) at age 60. The youngest nominees were Ryan Gosling (Half Nelson, 2006) and Heath Ledger (Brokeback Mountain, 2005) both at age 26. The oldest was Bruce Dern (Nebraska, 2013) at age 77.

Intriguingly, there also appears to be a relationship between age and box office (I highlighted the three outliers, highest grossing films, in red to emphasize their impact on the trend line):

What can be said statistically?

As I assumed, after examining the similarities between the two group’s means, none of the variables, after many attempted transformations, functional form specifications, and regression models, were a statistically significant indicator of winning for best actor. The model below reflects many failed attempts to reject a non-zero relationship— non-zero implying that a change in the predictor variable is correlated with a change in the dependent variable.

Here the statistical significance of the beta on natural log of box office revenue (I used natural log to make the relationship linear, although you can ignore this for our purposes) is rejected at the 10% significant level— basically, there is no reason to interpret it. However, where I did find some interesting correlations was conducting a log-linear regression of inflation-adjusted box office revenue on age, nationality, and winning the Oscar. Looking first at a model of Log(Box Office^) = Age^:

Rejecting the null hypothesis that there is no relationship at the 10% level (statistical speak), increasing the age of the lead actor by one year is, on average, associated with a 2.1% decrease in box office revenue. This result should be taken with a grain of salt, however, as the 95% confidence interval does include 0 (although just barely) and it likely suffers from severe omitted variable bias—which would invalidate these results. For example, older lead actors might garner a lower production budget from the studio, which would be correlated with both their age and the film’s box office revenue. Luckily, I have a couple more variables that I can include. Could the lead actor being American also be influencing the film’s revenue? Could the relative quality of the performance (winning the Oscar) be influencing revenue?

Controlling for the relative acting performances (whether it won the Oscar) and a possible home country bias (whether the actor is American), does lead to a slightly more accurate model. Age is now statistically significant at the 5% level and its 95% confidence interval no longer includes 0. Not surprisingly, and as we saw from the difference in film revenues earlier, whether the film won for best actor is not statistically significant. However, if the lead actor is American could potentially be influencing the box office revenues— it is statistically significant at the 10% level and suggests that an American lead actor is associated, on average, with a 50% increase in box office revenues (from the descriptive data, the average box office revenue for an American is $60 million versus $54.7 million for a non-American). Again though, I would urge caution, as there is likely serious omitted variable bias in this result. For example, whether the film is produced in America, as opposed to internationally, could be correlated with whether the lead actor is American and box office revenue.

Businesses have a long record of using athletes to market their products. The association between your brand and a popular athlete can induce otherwise-absent goodwill and recognition. More traditionally, a popular sporting event can attract millions of viewers, and millions of views of your brand. With annual global sporting revenues in excess of $140 billion, companies are willing to shell out big bucks for the right jock- with Nike’s $250 million, 10-year deal with golfer Rory McIlroy near the top.

I am not a fan of golf, but was nevertheless curious about the value of a golf sponsorship and how this value related to athlete performance. While most sponsorships now include ‘morality clauses’ to hedge against off-field indiscretions, I am guessing fewer have detailed performance metrics. Nevertheless, I presumed that for the sponsoring company, there is additional value in endorsing a winner (versus a runner-up, etc.). Given the typical PGA viewer demo- 27% earn $100k plus annually- I imagined that these results would manifest in the market through investor's anticipation of additional future publicity, perception of equipment performance (when applicable- i.e. Nike golf balls), and increased positive brand association for general consumers.

To explore this possibility, I looked at 44 major champions from 1997-2015 that sported visible endorsements from publicly traded companies (e.g. Nike hat or Titleist shirt). I hypothesized that, controlling for the daily fluctuation of the market, investors would- on the margin- perceive greater future value for the company from the added publicity and goodwill of the sponsored-athlete’s victory. This, I figured, would lead to these stocks beating a market index the day after a sponsored golfer’s victory, on average, over the 44 observations.

The graph below shows the trading results from a hypothetical $10,000 investment (either in a market index fund or the sponsor’s stock) the first day the exchange opens after the championship:

Overall, the stock returns were split with 22 posting gains and 22 in the red. The gains were slightly larger than the losses with an average gain of +0.13% covering all 44 stock investments. Sadly for my thesis, the market indexes outperformed the stocks. The indexes posted gains on 28 days of trading against 16 losses, with the average index return over the 48 trading days standing at +0.28%. Making two initial $10K investments- and allowing for the hypothetical trading of stocks/indexes without a capital gains tax- the following chart shows the results from reallocating the investment to the respective stock/index for one day after each of the 44 major championships:

On a $10,000 dollar initial investment, the stock investment would be worth $10,566 after the ‘44th day’ of trading and the index investment would be worth $11,323- or 7.1% greater. The following chart shows the daily difference in a unique $10,000 investment for each of the 44 trading days (stock outcome – index outcome):

From this graph you can see that while the index generally outperforms the stock, the trend line indicates a shift toward the stock outperforming the index. This graph also draws attention to the large negative outlier on the 5th day of trading; an observation that represents Tiger Woods, sponsored by Nike, winning the U.S. Open on June 18, 2000. On the following day of trading, Nike’s stock dropped by 3.5% while the NYSE closed up 0.7%. This equated to a $420 dollar net difference between two $10,000 investments- more than twice as large as the next biggest loss. Nike has had a rough start to the 21st century in its sporting endorsements; Michael Vick, Tiger Woods, and Lance Armstrong each doing irreparable harm to their personal brands- and by association Nike. Locking-up Rory McIlroy, a young and talented golfer yet to enter his prime, may be a smart long-term advertising strategy- sadly, the market didn’t seem to care much after his first major championship as a Nike-man; its stock dropped 0.5% as the market edged-up ever so slightly.

Escaping ISIS, the new documentary from PBS’s Frontline, is gut-wrenching in its images of slavery and sexual violence in the ISIS-occupied territories of Syria and Iraq. The story pieced together by British producer and director Edward Watts, focuses on the plight of the dwindling and oft-misunderstood Yazidi minority. Emerging into the public eye last summer during their exodus from ISIS fighters on Mount Sinjar in a remote part of Northern Iraq, the appearance of US and UK Special Forces to shepherd the Yazidi refugees to Kurdish territory marked the first salvo of Western confrontation with the Islamic State and the beginning of a new struggle for Yazidi survival. Click here for a link to the video from PBS.

Who are the Yazidis?

Yazidi faith intertwines elements of the Abrahamic religions with Persian mysticism and local tradition. With uncertain origins- although commonly thought to have begun in Kurdistan in the 12th century- and introverted communal practices, the Yazidis have long drawn undesirable attention from outsiders that have viewed them as devil worshippers and polytheists. Neither of these accusations are true. The Yazidis share a beautiful oral tradition that incorporates Christian and Muslim practices with the belief of transmigration and the indirect worship of one supreme god and his angels. Their faith is a mosaic of the ancient traditions of the region, that doesn’t proselytize, and seeks gradual enlightenment and purification through peaceful worship. Practicing as a minority in a chaotic region, there are estimated to be anywhere between 70,000 and 500,000 adherents remaining in the world.

The Documentary

The documentary picks up some months after Western and Kurdish operations on Mount Sinjar in August 2014. It is the aftermath of the ISIS onslaught and some refugees have been saved, many slaughtered, and many women sold into slavery to act as concubines to ISIS fighters. ISIS does nothing to hide the fate of those who challenge their perverted ideology and Escaping ISIS pulls no punches in showing or detailing the gruesomeness- mass execution, stoning, and sexual violence are common. Escaping ISIS, as its name implies, tells the stories of those who have escaped ISIS captivity and of one Yazidi lawyer, Khalil al-Dakhi’s network to rescue others. Armed with cellphones, a database of the thousands missing and believed captured, and crude Google maps, al-Dakhi and his underground network are nothing short of heroic in their forays into ISIS territory. While Escaping ISIS shocks the soul of any viewer with its depiction of violence, it strikes a resilient tone that resonates with a slither of hope.Where is the United States?As an American watching Escaping ISIS, there is no escaping a sinking sense of culpability for the bedlam- regardless of how you apportion the blame across administrations. The cavernous wound torn into the Middle East with the deposing of Saddam Hussein fundamentally shifted the balance of power in a region that was fragile from its Sykes-Picot origins. The great author and former-Washington Post correspondent Thomas Ricks studiously documented the Fiascothat was the administration of post-Baathist Iraq and famously warned that the real war for Iraq had yet to be fought. Sadly, he was right and the U.S., justifiably or not, will likely play a marginal role.

Ignoring domestic political considerations, two key historical lessons color Obama administration policy against ISIS: (1) the arms supplied to un-vetted rebel groups in Afghanistan in the 1980s were used to kill Americans in the 2000s and (2) Western ground troops in the Middle East (Lebanon 1983, Iraq 2003) can’t pacify a population they don’t understand and can’t communicate with- to say nothing of the cost in lives and dollars. The Obama administration, with the best intentions, was prepared to strike a fresh path of soft power and diplomacy rather than directly manage the fallout of the Arab spring and the rebuilding of Iraq. Unfortunately, what has manifested, rather than the likely worst-case scenario of local sectarian war, is a regional and apocalyptic menace seeking to lay national foundations for international terror. Belated attempts by the Obama administration to organize and arm a rebel force for Syria have resulted in comically bad early performance- with less than 100 of the 5000 desired fighters trained. While airstrikes have stemmed advance in some areas and eliminated some influential leaders, ISIS continues to advance in Syria and Iraq and acquire ‘emirates’ in Africa and the Caucasus. This year, Tony Blair’s former chief of staff published a manifesto advocating negotiation with terrorists. While I admittedly haven’t read it, and it is a better strategy for the Taliban than for ISIS (the latter with whom I wouldn't advocate it now), I applaud any outside the box thinking on a path forward. In the mean time, I can only pray that U.S. intelligence services are providing financial and strategic resources to the likes of Khalil al-Dhaki and others that are willing to confront ISIS, not as proxies, but as pillars of reason and peace.

Following Sunday’s rejection of its latest- yet expired- bailout terms in a national referendum, there has been growing fear that Greece may soon exit the Euro. Greece’s Syriza government- elected in January by popular disdain for the austerity measures imposed by its creditors- has been reluctant to continue with the structural reforms initiated under previous bailout agreements and impolitic in its diplomacy with its financiers. Who is to blame? In short, everyone. The International Monetary Fund, holders of €48.1 billion in Greek Debt, released an internal report Friday concluding that while Greece failed to make meaningful progress toward liberalizing their economy, even if they had, it would have been exceedingly optimistic to think they could have ever paid their debts in full. The Wall Street Journal has an excellent graphic showing the owners of Greek debt and the current timeline for repayment:

If it seemed unlikely that Greece would be able to pay back its debts in the long run (~175% of its GDP), why continue to loan it the money to do so? Two main reasons: ·Acknowledging that it would be unable to pay its debts, and therefore having lenders write substantial portions of it off, would create a precedent for other indebted Eurozone countries to refuse reform measures and demand debt write-offs (ex. Portugal, Spain, Ireland). Populist governments would be emboldened, the cost of borrowing would grow exponentially as market credibility diminished, and the politics of the EU would become ever more toxic- threatening incumbent/establishment politicians across the spectrum. ·Eventually, if not immediately, the biggest losers if the Greeks are to default on some or all of their debt are other European taxpayers. The vast majority of Greek debt is held directly by European state governments, European institutions (whose ‘shareholders’ are European states) and by private European banks. Billion dollar losses would trickle down through pension funds, shareholdings, and central bank sheets to the streets of Paris and Berlin. So what is likely to happen? Despite the vitriolic rhetoric crisscrossing the continent between Brussels and Athens, the fundamental pressures for both sides to cut a deal have not changed. The Greek government is still aware of the disaster that would ensue from adopting the Drachma, dropping the Euro, leaving the European Union, and being ostracized by international finance markets. European governments are still cognizant that intransigence on moderating their bailout terms- and by default accepting nothing over something- will be disastrous both politically and economically. What therefore seems most likely is that European governments agree to gorge a politically poisonous pill and forgive a percentage of Greek debt in exchange for minor structural concessions from the Greek government- who will walk away looking prescient and victorious to their constituents, despite having been to the edge of the abyss. If this result is realized, its details still leave the future uncertain: will the debt reduction amount to postponing the crisis or economic renewal? Will voters in Spain elect Podemos and play their own round of Russian roulette with the EU? Will David Cameron be emboldened to demand more autonomy for the UK in the run-up to his referendum on Europe? Will the German foundation of Europe begin to crack? Whatever happens this week, there will be few answers and many questions.

Out of the 115.6 million homes with televisions in 2014, approximately 7.8 million tuned in for The Bachelor season 19 premier- 7% of all TV viewership at 8pm EST that Monday night.﻿ Of these 7.8 million, it is likely that a sizable portion of loyal fans were gambling on the show’s outcome- as measured by a contestant’s longevity before being eliminated based on the contestant’s biographical data (which on the ABC homepage includes a photo, their age, occupation, and hometown). Whether the gambling was formal- there exist a number of online sports books that allow for betting on reality television despite information that can leak prior to the show airing- or informal- pools and drafts that may or may not include a financial reward- the show has potentially produced enough data over its 19 seasons for a savvy gambler to position him/herself statistically. Given the data available on the contestants before the season premier, and accounting for attractiveness not being readily or objectively quantifiable, an analysis can be performed that looks at the week a contestant was eliminated versus their age, hometown, and occupation. The data was compiled for seasons 11- 19 and included 239 contestants. A contestant’s outcome was measured to be the week they were eliminated, with a higher number week being a better outcome than a lower one. Elimination weeks ranged from 1 to 11 (for seasons 15-19) and 1 to 9 (for seasons 11-15) with the winner being coded ‘week 11’ (or ‘week 9’) and the runner-up being coded ‘week 10’ (or ‘week 8’). Age required no coding and was entered directly from the biographical data. The average age was 26.7 years with a high of 36 and a low of 21.

For occupation, I decided to create a binary variable that would be equal to 1 if the contestant’s job required a college degree and 0 if it did not. If it was not obvious that the listed position required a college degree (i.e. ‘account manager’) then it was coded as a 0. This required some judgment on my part and it is possible contestants are incorrectly coded. Overall, 86 out of 238 (36%) contestants were coded as having a college degree. For hometown, I decided to create a binary variable that would provide a proxy for ‘Southern’. I coded the hometown as 1 if the contestant was from a state that voted for Mitt Romney in the 2012 presidential campaign and as 0 if they were from a state that voted for Barack Obama- international contestants were coded as 0. Overall, 68 out of 238 (29%) contestants originated from a state that supported Romney in 2012. Next, I wanted to use regression analysis to see how these variables (age, hometown, and occupation) were associated with the number of weeks a contestant remained on the show. Of course, there are many other omitted variables that impact longevity and the choice of ‘bachelor’ impacts the desired attributes of a contestant. This analysis is merely designed to identify past correlations. First, I ran a regression of age on elimination week:

While the coefficient on age is negative (meaning that as age increases their longevity or week eliminated decreases), it is not statistically significant- even at the 10% level. This means that it is likely that if the true relationship between age and elimination week were 0 (i.e. they are not related) we would find an effect as large as the one we found 19.2% of the time. We therefore fail to reject this null hypothesis and can’t conclude that age impacts a contestant’s longevity on the show.

Second, I ran a regression of occupation on elimination week:

Again, while the coefficient on occupation is negative, it is not statistically significant at the 10% level- in fact this time it is not even close. This means that while there is a very loose negative correlation between holding a college degree and elimination week, it can’t be distinguished from a purely random result. Third, I ran a regression of hometown on elimination week:

In this regression, there is a statistically significant positive relationship at the 1% level. What does this mean? It means that if being from a state that voted for Mitt Romney had no real impact on your elimination week, we would only find a result as large as what we found in 0.006% of samples. This means we can conclude the following: being from a state that voted for Mitt Romney in 2012 is associated on average with remaining on the Bachelor for an additional 1.085 weeks.

So while being older and educated may not have a true negative relationship with contestant longevity, it appears to have been a very good bet to choose contestants from politically conservative states over the past 8 seasons- and to continue choosing them in the future.